1.Flink数据源

        Flink可以从各种数据源获取数据，然后构建DataStream 进行处理转换。source就是整个数据处理程序的输入端。

数据集合

数据文件

Socket数据

kafka数据

自定义Source

2.案例

2.1.从集合中获取数据

        创建 FlinkSource_List 类，再创建个 Student 类（姓名、年龄、性别三个属性就行，反正测试用）

package com.qiyu;import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import java.util.ArrayList;/*** @author MR.Liu* @version 1.0* @data 2023-10-18 16:13*/
public class FlinkSource_List {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);ArrayList<Student> clicks = new ArrayList<>();clicks.add(new Student("Mary",25,1));clicks.add(new Student("Bob",26,2));DataStream<Student> stream = env.fromCollection(clicks);stream.print();env.execute();}
}

运行结果：

Student{name='Mary', age=25, sex=1}
Student{name='Bob', age=26, sex=2}

2.2.从文件中读取数据

文件数据：

spark
hello world kafka spark
hadoop spark

package com.qiyu;import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;/*** @author MR.Liu* @version 1.0* @data 2023-10-18 16:31*/
public class FlinkSource_File {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);DataStream<String> stream = env.readTextFile("input/words.txt");stream.print();env.execute();}
}

运行结果：（没做任何处理）

spark
hello world kafka spark
hadoop spark

2.3.从Socket中读取数据

package com.qiyu;import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;/*** @author MR.Liu* @version 1.0* @data 2023-10-18 17:41*/
public class FlinkSource_Socket {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();// 2. 读取文本流DataStreamSource<String> lineDSS = env.socketTextStream("192.168.220.130",7777);lineDSS.print();env.execute();}
}

运行结果：

服务器上执行：

 nc -lk 7777

疯狂输出

控制台打印结果 

6> hello
7> world

2.4.从Kafka中读取数据

pom.xml 添加Kafka连接依赖

      <dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency>

package com.qiyu;import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;import java.util.Properties;/*** @author MR.Liu* @version 1.0* @data 2023-10-19 10:01*/
public class FlinkSource_Kafka {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);Properties properties = new Properties();properties.setProperty("bootstrap.servers", "hadoop102:9092");properties.setProperty("group.id", "consumer-group");properties.setProperty("key.deserializer","org.apache.kafka.common.serialization.StringDeserializer");properties.setProperty("value.deserializer","org.apache.kafka.common.serialization.StringDeserializer");properties.setProperty("auto.offset.reset", "latest");DataStreamSource<String> stream = env.addSource(new FlinkKafkaConsumer<String>("clicks", new SimpleStringSchema(), properties));stream.print("Kafka");env.execute();}
}

启动 zk 和kafka

创建topic

bin/kafka-topics.sh --create --bootstrap-server hadoop102:9092 --replication-factor 1 --partitions 1 --topic clicks

生产者、消费者命令

bin/kafka-console-producer.sh --bootstrap-server hadoop102:9092  --topic clicks
bin/kafka-console-consumer.sh --bootstrap-server hadoop102:9092  --topic clicks --from-beginning

启动生产者命令后疯狂输入 

运行java类，运行结果：和生产者输入的是一样的

Kafka> flinks
Kafka> hadoop
Kafka> hello
Kafka> nihaop

2.5.从自定义Source中读取数据

        大多数情况下，前面几个数据源已经满足需求了。但是遇到特殊情况我们需要自定义的数据源。实现方式如下：

        1.编辑自定义源Source

package com.qiyu;import org.apache.flink.streaming.api.functions.source.SourceFunction;import java.util.Calendar;
import java.util.Random;/*** @author MR.Liu* @version 1.0* @data 2023-10-19 10:37*//**** 主要实现2个方法 run() 和 cancel()*/
public class FlinkSource_Custom implements SourceFunction<Student> {// 声明一个布尔变量，作为控制数据生成的标识位private Boolean running = true;@Overridepublic void run(SourceContext<Student> sourceContext) throws Exception {Random random = new Random(); // 在指定的数据集中随机选取数据String[] name = {"Mary", "Alice", "Bob", "Cary"};int[] sex = {1,2};int age = 0;while (running) {sourceContext.collect(new Student(name[random.nextInt(name.length)],sex[random.nextInt(sex.length)],random.nextInt(100)));// 隔 1 秒生成一个点击事件，方便观测Thread.sleep(1000);}}@Overridepublic void cancel() {running = false;}
}

        2.编写主程序

package com.qiyu;import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;/*** @author MR.Liu* @version 1.0* @data 2023-10-19 10:46*/
public class FlinkSource_Custom2 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);
//有了自定义的 source function，调用 addSource 方法DataStreamSource<Student> stream = env.addSource(new FlinkSource_Custom());stream.print("SourceCustom");env.execute();}
}

 运行主程序，运行结果：

SourceCustom> Student{name='Mary', age=1, sex=46}
SourceCustom> Student{name='Cary', age=2, sex=52}
SourceCustom> Student{name='Bob', age=1, sex=14}
SourceCustom> Student{name='Alice', age=1, sex=84}
SourceCustom> Student{name='Alice', age=2, sex=82}
SourceCustom> Student{name='Cary', age=1, sex=28}

.............